Informatica

The article describes multi-function system testing based on fusion (or revelation) of clique-like structures. The following sets are considered: (i) subsystems (system parts or units/components/modules), (ii) system functions and a subset of system components for each system function, and (iii) function clusters (some groups of system functions which are used jointly). Test procedures (as units testing) are used for each subsystem. The procedures lead to an ordinal result (states, colors) for each component (e.g., ‘out of service’, ‘major faults’, ‘minor faults’, ‘trouble free service’). For each system function a graph over corresponding system components is examined while taking into account ordinal estimates/colors of the components. Further, an integrated graph for each function cluster is considered (this graph integrates the graphs for corresponding system functions). For the integrated graph structure revelation problems are under examination (revelation of some subgraphs which can lead to system faults). Numerical examples illustrate the approach and problems.

In this paper, an efficient hybrid genetic algorithm (HGA) and its variants for the well-known combinatorial optimization problem, the quadratic assignment problem (QAP) are discussed. In particular, we tested our algorithms on a special type of QAPs, the structured quadratic assignment problems. The results from the computational experiments on this class of problems demonstrate that HGAs allow to achieve near-optimal and (pseudo-)optimal solutions at very reasonable computation times. The obtained results also confirm that the hybrid genetic algorithms are among the most suitable heuristic approaches for this type of QAPs.

Real life scheduling problems are solved by heuristics with parameters defined by experts, as usual. In this paper a new approach is proposed where the parameters of various heuristics and their random mixtures are optimized to reduce the average deviations from the global optimum.

Many heuristics, such as simulated annealing, genetic algorithms, greedy randomized adaptive search procedures are stochastic. In this paper, we propose a deterministic heuristic algorithm, which is applied to the quadratic assignment problem. We refer this algorithm to as intensive search algorithm (or briefly intensive search). We tested our algorithm on the various instances from the library of the QAP instances – QAPLIB. The results obtained from the experiments show that the proposed algorithm appears superior, in many cases, to the well-known algorithm – simulated annealing.

In this paper we deal with the problem of extremal parameter grouping. The problem formulation, the algorithms of parameter grouping and the fields of implementation are presented. The deterministic algorithms of extremal parameter grouping often find the local maximum of the functional, characterizing the quality of a partition. The problem has been formulated as a problem of combinatorial optimization and attempted to be solved using the simulated annealing strategy. The algorithms, realizing such a strategy and devoted to the solving of the problem concerned, are proposed and investigated.